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              <p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../../getting_started/installation.html">Installation</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../getting_started/jetpack.html">Torch-TensorRT in JetPack</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../getting_started/quick_start.html">Quick Start</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../getting_started/capture_and_replay.html">Introduction</a></li>
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<p class="caption" role="heading"><span class="caption-text">User Guide</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../../user_guide/torch_tensorrt_explained.html">Torch-TensorRT Explained</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../user_guide/dynamic_shapes.html">Dynamic shapes with Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../user_guide/saving_models.html">Saving models compiled with Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../user_guide/runtime.html">Deploying Torch-TensorRT Programs</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../user_guide/using_dla.html">DLA</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../user_guide/mixed_precision.html">Compile Mixed Precision models with Torch-TensorRT</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
<ul class="current">
<li class="toctree-l1"><a class="reference internal" href="torch_compile_advanced_usage.html">Torch Compile Advanced Usage</a></li>
<li class="toctree-l1"><a class="reference internal" href="vgg16_ptq.html">Deploy Quantized Models using Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="engine_caching_example.html">Engine Caching</a></li>
<li class="toctree-l1"><a class="reference internal" href="engine_caching_bert_example.html">Engine Caching (BERT)</a></li>
<li class="toctree-l1"><a class="reference internal" href="refit_engine_example.html">Refitting Torch-TensorRT Programs with New Weights</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
<li class="toctree-l1"><a class="reference internal" href="torch_export_cudagraphs.html">Torch Export with Cudagraphs</a></li>
<li class="toctree-l1"><a class="reference internal" href="converter_overloading.html">Overloading Torch-TensorRT Converters with Custom Converters</a></li>
<li class="toctree-l1"><a class="reference internal" href="custom_kernel_plugins.html">Using Custom Kernels within TensorRT Engines with Torch-TensorRT</a></li>
<li class="toctree-l1 current"><a class="current reference internal" href="#">Automatically Generate a Converter for a Custom Kernel</a></li>
<li class="toctree-l1"><a class="reference internal" href="auto_generate_plugins.html">Automatically Generate a Plugin for a Custom Kernel</a></li>
<li class="toctree-l1"><a class="reference internal" href="mutable_torchtrt_module_example.html">Mutable Torch TensorRT Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="weight_streaming_example.html">Weight Streaming</a></li>
<li class="toctree-l1"><a class="reference internal" href="pre_allocated_output_example.html">Pre-allocated output buffer</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Dynamo Frontend</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../../dynamo/torch_compile.html">TensorRT Backend for <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code></a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../dynamo/dynamo_export.html">Compiling Exported Programs with Torch-TensorRT</a></li>
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<p class="caption" role="heading"><span class="caption-text">TorchScript Frontend</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../../ts/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../ts/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../ts/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../ts/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../ts/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../ts/ptq.html">Post Training Quantization (PTQ)</a></li>
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<p class="caption" role="heading"><span class="caption-text">FX Frontend</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../../fx/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
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<p class="caption" role="heading"><span class="caption-text">Model Zoo</span></p>
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<li class="toctree-l1"><a class="reference internal" href="torch_compile_resnet_example.html">Compiling ResNet with dynamic shapes using the <cite>torch.compile</cite> backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="torch_compile_transformers_example.html">Compiling BERT using the <cite>torch.compile</cite> backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="torch_compile_stable_diffusion.html">Compiling Stable Diffusion model using the <cite>torch.compile</cite> backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../compile_hf_models.html">Compiling LLM models from Huggingface</a></li>
<li class="toctree-l1"><a class="reference internal" href="torch_compile_gpt2.html">Compiling GPT2 using the Torch-TensorRT <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> frontend</a></li>
<li class="toctree-l1"><a class="reference internal" href="torch_export_sam2.html">Compiling SAM2 using the dynamo backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="torch_export_flux_dev.html">Compiling FLUX.1-dev model using the Torch-TensorRT dynamo backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../notebooks.html">Legacy notebooks</a></li>
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<p class="caption" role="heading"><span class="caption-text">Python API Documentation</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../../py_api/torch_tensorrt.html">torch_tensorrt</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../py_api/dynamo.html">torch_tensorrt.dynamo</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../../../py_api/fx.html">torch_tensorrt.fx</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../../../py_api/ptq.html">torch_tensorrt.ts.ptq</a></li>
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<p class="caption" role="heading"><span class="caption-text">C++ API Documentation</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../../_cpp_api/torch_tensort_cpp.html">Torch-TensorRT C++ API</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../_cpp_api/namespace_torch_tensorrt.html">Namespace torch_tensorrt</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../_cpp_api/namespace_torch_tensorrt__logging.html">Namespace torch_tensorrt::logging</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../_cpp_api/namespace_torch_tensorrt__torchscript.html">Namespace torch_tensorrt::torchscript</a></li>
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<p class="caption" role="heading"><span class="caption-text">CLI Documentation</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../../cli/torchtrtc.html">torchtrtc</a></li>
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<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../../contributors/system_overview.html">System Overview</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../contributors/dynamo_converters.html">Writing Dynamo Converters</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../contributors/writing_dynamo_aten_lowering_passes.html">Writing Dynamo ATen Lowering Passes</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../contributors/ts_converters.html">Writing TorchScript Converters</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../contributors/useful_links.html">Useful Links for Torch-TensorRT Development</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../contributors/resource_management.html">Resource Management</a></li>
</ul>
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  <div class="sphx-glr-download-link-note admonition note">
<p class="admonition-title">Note</p>
<p><a class="reference internal" href="#sphx-glr-download-tutorials-rendered-examples-dynamo-auto-generate-converters-py"><span class="std std-ref">Go to the end</span></a>
to download the full example code</p>
</div>
<section class="sphx-glr-example-title" id="automatically-generate-a-converter-for-a-custom-kernel">
<span id="auto-generate-converters"></span><span id="sphx-glr-tutorials-rendered-examples-dynamo-auto-generate-converters-py"></span><h1>Automatically Generate a Converter for a Custom Kernel<a class="headerlink" href="#automatically-generate-a-converter-for-a-custom-kernel" title="Permalink to this heading">¶</a></h1>
<p>We are going to demonstrate how to automatically generate a converter for a custom kernel using Torch-TensorRT using
the new Python based plugin system in TensorRT 10.8.</p>
<p>Torch-TensorRT supports falling back to PyTorch implementations of operations in the case that Torch-TensorRT
does not know how to compile them in TensorRT. However, this comes at the cost of a graph break and will reduce the performance of the model.
The easiest way to fix lack of support for ops is by adding a decomposition (see:
<a class="reference external" href="https://pytorch.org/TensorRT/contributors/writing_dynamo_aten_lowering_passes.html">Writing lowering passes for the Dynamo frontend</a>) - which defines the operator
in terms of PyTorch ops that are supported in Torch-TensorRT or a converter (see:
<a class="reference external" href="https://pytorch.org/TensorRT/contributors/dynamo_converters.html">Writing converters for the Dynamo frontend</a>) - which defines the operator in terms of TensorRT operators.</p>
<p>In some cases there isn’t a great way to do either of these, perhaps because the operator is a custom kernel that is not part of standard PyTorch or
TensorRT cannot support it natively.</p>
<p>For these cases, it is possible to use a TensorRT plugin to replace the operator <strong>inside</strong> the TensorRT engine, thereby avoiding
the performance and resource overhead from a graph break.</p>
<p>Previously this involved a complex process in not only building a performant kernel but setting it up to run in TensorRT (see: <a class="reference external" href="https://pytorch.org/TensorRT/tutorials/_rendered_examples/dynamo/custom_kernel_plugins.html">Using Custom Kernels within TensorRT Engines with Torch-TensorRT</a>).
With TensorRT 10.8, there is a new Python native plugin system which greatly streamlines this process. This
plugin system also allows Torch-TensorRT to automatically generate the necessary conversion code to convert the
operation in PyTorch to TensorRT.</p>
<section id="writing-custom-operators-in-pytorch">
<h2>Writing Custom Operators in PyTorch<a class="headerlink" href="#writing-custom-operators-in-pytorch" title="Permalink to this heading">¶</a></h2>
<blockquote>
<div><p>Pervious tutorials already cover creating custom operators in PyTorch which later get used with Torch-TensorRT.</p>
</div></blockquote>
<p>Here we define a simple elementwise multiplication operator in Triton. This operator is then registered as a custom op in PyTorch.
with its host launch code as well as a “meta-kernel”, A meta-kernel is a function that describes the shape and data type
transformations that the operator will perform. This meta-kernel is used by Dynamo and Torch-TensorRT, so it
is necessary to define.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">Tuple</span>

<span class="kn">import</span><span class="w"> </span><span class="nn">tensorrt.plugin</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">trtp</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">torch</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">torch_tensorrt</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">triton</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">triton.language</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">tl</span>


<span class="nd">@triton</span><span class="o">.</span><span class="n">jit</span>
<span class="k">def</span><span class="w"> </span><span class="nf">elementwise_mul_kernel</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">BLOCK_SIZE</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">):</span>
    <span class="c1"># Program ID determines the block of data each thread will process</span>
    <span class="n">pid</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">program_id</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
    <span class="c1"># Compute the range of elements that this thread block will work on</span>
    <span class="n">block_start</span> <span class="o">=</span> <span class="n">pid</span> <span class="o">*</span> <span class="n">BLOCK_SIZE</span>
    <span class="c1"># Range of indices this thread will handle</span>
    <span class="n">offsets</span> <span class="o">=</span> <span class="n">block_start</span> <span class="o">+</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_SIZE</span><span class="p">)</span>
    <span class="c1"># Load elements from the X and Y tensors</span>
    <span class="n">x_vals</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">X</span> <span class="o">+</span> <span class="n">offsets</span><span class="p">)</span>
    <span class="n">y_vals</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">Y</span> <span class="o">+</span> <span class="n">offsets</span><span class="p">)</span>
    <span class="c1"># Perform the element-wise multiplication</span>
    <span class="n">z_vals</span> <span class="o">=</span> <span class="n">x_vals</span> <span class="o">*</span> <span class="n">y_vals</span>
    <span class="c1"># Store the result in Z</span>
    <span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">Z</span> <span class="o">+</span> <span class="n">offsets</span><span class="p">,</span> <span class="n">z_vals</span><span class="p">)</span>


<span class="nd">@torch</span><span class="o">.</span><span class="n">library</span><span class="o">.</span><span class="n">custom_op</span><span class="p">(</span><span class="s2">&quot;torchtrt_ex::elementwise_mul&quot;</span><span class="p">,</span> <span class="n">mutates_args</span><span class="o">=</span><span class="p">())</span>  <span class="c1"># type: ignore[misc]</span>
<span class="k">def</span><span class="w"> </span><span class="nf">elementwise_mul</span><span class="p">(</span>
    <span class="n">X</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">Y</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">b</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.2</span><span class="p">,</span> <span class="n">a</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
    <span class="c1"># Ensure the tensors are on the GPU</span>
    <span class="k">assert</span> <span class="n">X</span><span class="o">.</span><span class="n">is_cuda</span> <span class="ow">and</span> <span class="n">Y</span><span class="o">.</span><span class="n">is_cuda</span><span class="p">,</span> <span class="s2">&quot;Tensors must be on CUDA device.&quot;</span>
    <span class="k">assert</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">Y</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="s2">&quot;Tensors must have the same shape.&quot;</span>

    <span class="c1"># Create output tensor</span>
    <span class="n">Z</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty_like</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>

    <span class="c1"># Define block size</span>
    <span class="n">BLOCK_SIZE</span> <span class="o">=</span> <span class="mi">1024</span>

    <span class="c1"># Grid of programs</span>
    <span class="n">grid</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">meta</span><span class="p">:</span> <span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span> <span class="o">//</span> <span class="n">meta</span><span class="p">[</span><span class="s2">&quot;BLOCK_SIZE&quot;</span><span class="p">],)</span>

    <span class="c1"># Launch the kernel</span>
    <span class="n">elementwise_mul_kernel</span><span class="p">[</span><span class="n">grid</span><span class="p">](</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">BLOCK_SIZE</span><span class="o">=</span><span class="n">BLOCK_SIZE</span><span class="p">)</span>

    <span class="k">return</span> <span class="n">Z</span>
</pre></div>
</div>
<p>The meta kernel for an elementwise operation is just the shape and dtype of one of the inputs since we will not change the shape
in the course of the operation.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@torch</span><span class="o">.</span><span class="n">library</span><span class="o">.</span><span class="n">register_fake</span><span class="p">(</span><span class="s2">&quot;torchtrt_ex::elementwise_mul&quot;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">b</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.2</span><span class="p">,</span> <span class="n">a</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
    <span class="k">return</span> <span class="n">x</span>
</pre></div>
</div>
</section>
<section id="writing-plugins-for-tensorrt-using-the-quick-deploy-plugin-system">
<h2>Writing Plugins for TensorRT using the Quick Deploy Plugin system<a class="headerlink" href="#writing-plugins-for-tensorrt-using-the-quick-deploy-plugin-system" title="Permalink to this heading">¶</a></h2>
<p>The quick deployment plugin system in TensorRT 10.8 allows for the creation of custom plugins in Python with significantly
less boilerplate. It uses a similar system PyTorch where you define a function that describes the shape and data type transformations
that the operator will perform and then define the code to launch the kernel given GPU memory handles.</p>
<p>Just like the PyTorch meta kernel, there is no transformation in shape or data type between the input and output so
we can just tell TensorRT to expect the same shape as we get in</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@trtp</span><span class="o">.</span><span class="n">register</span><span class="p">(</span><span class="s2">&quot;torchtrt_ex::elementwise_mul&quot;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_</span><span class="p">(</span>
    <span class="n">x</span><span class="p">:</span> <span class="n">trtp</span><span class="o">.</span><span class="n">TensorDesc</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">trtp</span><span class="o">.</span><span class="n">TensorDesc</span><span class="p">,</span> <span class="n">b</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span> <span class="n">a</span><span class="p">:</span> <span class="nb">int</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">trtp</span><span class="o">.</span><span class="n">TensorDesc</span><span class="p">]:</span>
    <span class="k">return</span> <span class="n">x</span><span class="o">.</span><span class="n">like</span><span class="p">()</span>
</pre></div>
</div>
<p>Here we reuse similar host launch code as PyTorch but we need to convert the TensorRT tensors into PyTorch tensors prior to launching the kernel
These operations are also in-place, so the result must be put in the the output tensors provided by TensorRT.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@trtp</span><span class="o">.</span><span class="n">impl</span><span class="p">(</span><span class="s2">&quot;torchtrt_ex::elementwise_mul&quot;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_</span><span class="p">(</span>
    <span class="n">x</span><span class="p">:</span> <span class="n">trtp</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
    <span class="n">y</span><span class="p">:</span> <span class="n">trtp</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
    <span class="n">b</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
    <span class="n">a</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
    <span class="n">outputs</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">trtp</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span>
    <span class="n">stream</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
<span class="p">):</span>
    <span class="c1"># Define block size</span>
    <span class="n">BLOCK_SIZE</span> <span class="o">=</span> <span class="mi">1024</span>

    <span class="c1"># Grid of programs</span>
    <span class="n">grid</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">meta</span><span class="p">:</span> <span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span> <span class="o">//</span> <span class="n">meta</span><span class="p">[</span><span class="s2">&quot;BLOCK_SIZE&quot;</span><span class="p">],)</span>

    <span class="n">x_t</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
    <span class="n">y_t</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
    <span class="n">z_t</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span><span class="n">outputs</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">device</span><span class="o">=</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
    <span class="c1"># Launch the kernel</span>
    <span class="n">elementwise_mul_kernel</span><span class="p">[</span><span class="n">grid</span><span class="p">](</span><span class="n">x_t</span><span class="p">,</span> <span class="n">y_t</span><span class="p">,</span> <span class="n">z_t</span><span class="p">,</span> <span class="n">BLOCK_SIZE</span><span class="o">=</span><span class="n">BLOCK_SIZE</span><span class="p">)</span>
</pre></div>
</div>
</section>
<section id="generating-the-converter">
<h2>Generating the Converter<a class="headerlink" href="#generating-the-converter" title="Permalink to this heading">¶</a></h2>
<p>Given that we have defined the custom operator in PyTorch and TensorRT, we can now generate the converter for the operation.
As long as the namespace and names match, the following function will automatically generate the converter for the operation.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">dynamo</span><span class="o">.</span><span class="n">conversion</span><span class="o">.</span><span class="n">plugins</span><span class="o">.</span><span class="n">generate_plugin_converter</span><span class="p">(</span>
    <span class="s2">&quot;torchtrt_ex::elementwise_mul&quot;</span><span class="p">,</span> <span class="n">supports_dynamic_shapes</span><span class="o">=</span><span class="kc">True</span>
<span class="p">)</span>
</pre></div>
</div>
</section>
<section id="using-our-converter-with-a-model">
<h2>Using our converter with a model<a class="headerlink" href="#using-our-converter-with-a-model" title="Permalink to this heading">¶</a></h2>
<p>Now we can use our custom operator in a model and compile it with Torch-TensorRT.
We can see that the custom operator is used as one of the operations in the forward pass of the model.
The process of compiling the model at this point is identical to standard Torch-TensorRT usage.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">class</span><span class="w"> </span><span class="nc">MyModel</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>  <span class="c1"># type: ignore[misc]</span>
    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>

    <span class="k">def</span><span class="w"> </span><span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
        <span class="n">z</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
        <span class="n">res</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">torchtrt_ex</span><span class="o">.</span><span class="n">elementwise_mul</span><span class="o">.</span><span class="n">default</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">z</span><span class="p">,</span> <span class="n">a</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>

        <span class="k">return</span> <span class="n">res</span>


<span class="n">my_model</span> <span class="o">=</span> <span class="n">MyModel</span><span class="p">()</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
<span class="n">m</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">),</span> <span class="mi">2</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s2">&quot;cuda&quot;</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">)</span>
<span class="n">n</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">),</span> <span class="mi">3</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s2">&quot;cuda&quot;</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">)</span>

<span class="k">with</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">logging</span><span class="o">.</span><span class="n">errors</span><span class="p">():</span>
    <span class="n">model_trt</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">my_model</span><span class="p">,</span> <span class="n">inputs</span><span class="o">=</span><span class="p">[</span><span class="n">m</span><span class="p">,</span> <span class="n">n</span><span class="p">],</span> <span class="n">min_block_size</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
    <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">300</span><span class="p">):</span>
            <span class="n">res</span> <span class="o">=</span> <span class="n">model_trt</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span>
            <span class="k">assert</span> <span class="n">torch</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">res</span><span class="p">,</span> <span class="n">my_model</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">n</span><span class="p">))</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Ran with custom plugin!&quot;</span><span class="p">)</span>
</pre></div>
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